The following relates to the parking occupancy forecasting and guidance arts, and more generally to system state forecasting over short time horizons e.g. on the order of 1-20 minutes in some tasks, and to related arts.
In a common task, parking occupancy forecasting is desirably performed on a short time horizon of, for example, 2-10 minutes. As other illustrative tasks, it may be desired to forecast the number of jobs at a particular stage of a process in a print shop, or the number of people waiting in an emergency medical facility on a time scale over which it is possible to redeploy resources. Frequently, continuous time (semi-)Markov models are applied for such purposes. In these approaches, predictions are given by computing matrix exponentials. However, real-world systems are often more variable than such models predict. For instance, in parking occupancy forecasting, there may be variation in parking demand from day-to-day, and/or parking sensor observations may be subject to variable delays.